Method and Device for Voice Recognition Training

ABSTRACT

A method on a mobile device for voice recognition training is described. A voice training mode is entered. A voice training sample for a user of the mobile device is recorded. The voice training mode is interrupted to enter a noise indicator mode based on a sample background noise level for the voice training sample and a sample background noise type for the voice training sample. The voice training mode is returned to from the noise indicator mode when the user provides a continuation input that indicates a current background noise level meets an indicator threshold value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication 61/892,527, filed Oct. 18, 2013 and to U.S. ProvisionalPatent Application 61/857,696, filed Jul. 23, 2013, the contents of allof which are hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates to processing audio signals and, moreparticularly, to methods and devices for audio signals including voiceor speech.

BACKGROUND

Although speech recognition has been around for decades, the quality ofspeech recognition software and hardware has only recently reached ahigh enough level to appeal to a large number of consumers. One area inwhich speech recognition has become very popular in recent years is thesmartphone and tablet computer industry. Using a speechrecognition-enabled device, a consumer can perform such tasks as makingphone calls, writing emails, and navigating with GPS, strictly by voice.

Speech recognition in such devices is far from perfect, however. Whenusing a speech recognition-enabled device for the first time, the usermay need to “train” the speech recognition software to recognize his orher voice. For voice training of a voice recognition system to besuccessful, the user should be in an environment that meets certainlevels of criteria. For example, background noise levels during therecording of a voice training sample should be within an acceptablerange.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

While the appended claims set forth the features of the presenttechniques with particularity, these techniques, together with theirobjects and advantages, may be best understood from the followingdetailed description taken in conjunction with the accompanying drawingsof which:

FIG. 1 is a block diagram illustrating a mobile device, according to anembodiment;

FIG. 2 is a block diagram of example components of a mobile device,according to an embodiment;

FIG. 3 illustrates a process flow of a method for voice trainingrecognition that may be performed by the mobile device of FIG. 1,according to an embodiment;

FIGS. 4A, 4B, 4C, and 4D illustrate planar views of one example of auser interface of the mobile device of FIG. 1 for the process flow ofFIG. 3.

DETAILED DESCRIPTION

Turning to the drawings, wherein like reference numerals refer to likeelements, techniques of the present disclosure are illustrated as beingimplemented in a suitable environment. The following description isbased on embodiments of the claims and should not be taken as limitingthe claims with regard to alternative embodiments that are notexplicitly described herein.

When a user “trains” a voice or speech recognition system of a mobiledevice, the mobile device records a voice training sample. The mobiledevice analyzes the voice training sample for future verifications of avoice input from the user. Background noise present in the voicetraining sample increases a likelihood of error (e.g., false positive orfalse negative recognitions) for the future verifications. The mobiledevice determines a background noise level (e.g., in decibels) for thevoice training sample and provides feedback to the user regarding thebackground noise. For example, where a voice training sample hasbackground noise that exceeds a predetermined threshold, the mobiledevice may prompt the user for another voice training sample.

The mobile device also provides a visual indication of the currentbackground noise levels relative to the predetermined threshold. Thevisual indication allows the user to move to a more suitable locationfor providing the voice training sample. In addition to determining thebackground noise level, the mobile device may also determine abackground noise type for the voice training sample, such as stationarynoise (e.g., road noise inside a moving car or fan noise from a nearbycomputer) or non-stationary noise (e.g., sound from a television orconversation). Non-stationary noise generally has a higher variance insignal level (e.g., signal peaks when speaking and signal valleysbetween sentences) than stationary noise. Accordingly, the mobile devicemay use different thresholds based on the background noise type.

The various embodiments described herein allow a mobile device toindicate noise levels for a voice training sample recorded during avoice training mode. If the background noise level exceeds an indicatorthreshold level, the mobile device interrupts the voice training modeand enters a noise indicator mode. This reduces the likelihood ofrecording another voice training sample with excessive background noise.While in the noise indicator mode, the mobile device displays a noiseindicator interface with a noise indicator that corresponds to a currentbackground noise level for a received audio input signal. The noiseindicator has a disabled continuation indicator to prevent the user fromproceeding to the voice training mode. When the current background noiselevel meets an indicator threshold value, the mobile device enables thecontinuation indicator allowing the user to proceed by providing acontinuation input. If the continuation indicator is enabled, the mobiledevice returns to the voice training mode when the user provides thecontinuation input.

In one embodiment, the mobile device enters a voice training mode. Themobile device records a voice training sample for a user. The mobiledevice interrupts the voice training mode to enter a noise indicatormode based on a sample background noise level for the voice trainingsample and a sample background noise type for the voice training sample.The mobile device returns to the voice training mode from the noiseindicator mode when the user provides a continuation input thatindicates a current background noise level meets an indicator thresholdvalue.

Referring to FIG. 1, there is illustrated a perspective view of anexample mobile device 100. The mobile device 100 may be any type ofdevice capable of storing and executing multiple applications. Examplesof the mobile device 100 include, but are not limited to, mobiledevices, smart phones, smart watches, wireless devices, tablet computingdevices, personal digital assistants, personal navigation devices, touchscreen input device, touch or pen-based input devices, portable videoand/or audio players, and the like. It is to be understood that themobile device 100 may take the form of a variety of form factors, suchas, but not limited to, bar, tablet, flip/clam, slider, rotator, andwearable form factors.

For one embodiment, the mobile device 100 has a housing 101 comprising afront surface 103 which includes a visible display 105 and a userinterface. For example, the user interface may be a touch screenincluding a touch-sensitive surface that overlays the display 105. Foranother embodiment, the user interface or touch screen of the mobiledevice 100 may include a touch-sensitive surface supported by thehousing 101 that does not overlay any type of display. For yet anotherembodiment, the user interface of the mobile device 100 may include oneor more input keys 107. Examples of the input key or keys 107 include,but are not limited to, keys of an alpha or numeric keypad or keyboard,a physical keys, touch-sensitive surfaces, mechanical surfaces,multipoint directional keys and side buttons or keys 107. The mobiledevice 100 may also comprise a speaker 109 and microphone 111 for audiooutput and input at the surface. It is to be understood that the mobiledevice 100 may include a variety of different combination of displaysand interfaces.

The mobile device 100 includes one or more sensors 113 positioned at orwithin an exterior boundary of the housing 101. For example, asillustrated by FIG. 1, the sensor or sensors 113 may be positioned atthe front surface 103 and/or another surface (such as one or more sidesurfaces 115) of the exterior boundary of the housing 101. The sensor orsensors 113 may include an exterior sensor supported at the exteriorboundary to detect an environmental condition associated with anenvironment external to the housing. The sensor or sensors 113 may also,or in the alternative, include an interior sensors supported within theexterior boundary (i.e., internal to the housing) to detect a conditionof the device itself. Examples of the sensors 113 are described below inreference to FIG. 2.

Referring to FIG. 2, there is shown a block diagram representing examplecomponents (e.g., internal components) 200 of the mobile device 100 ofFIG. 1. In the present embodiment, the components 200 include one ormore wireless transceivers 201, one or more processors 203, one or morememories 205, one or more output components 207, and one or more inputcomponents 209. As already noted above, the mobile device 100 includes auser interface, including the touch screen display 105 that comprisesone or more of the output components 207 and one or more of the inputcomponents 209. Also as already discussed above, the mobile device 100includes a plurality of the sensors 113, several of which are describedin more detail below. In the present embodiment, the sensors 113 are incommunication with (so as to provide sensor signals to or receivecontrol signals from) a sensor hub 224.

Further, the components 200 include a device interface 215 to provide adirect connection to auxiliary components or accessories for additionalor enhanced functionality. In addition, the internal components 200include a power source or supply 217, such as a portable battery, forproviding power to the other internal components and allow portabilityof the mobile device 100. As shown, all of the components 200, andparticularly the wireless transceivers 201, processors 203, memories205, output components 207, input components 209, sensor hub 224, deviceinterface 215, and power supply 217, are coupled directly or indirectlywith one another by way of one or more internal communication link(s)218 (e.g., an internal communications bus).

Further, in the present embodiment of FIG. 2, the wireless transceivers201 particularly include a cellular transceiver 211 and a Wi-Fitransceiver 213. Although in the present embodiment the wirelesstransceivers 201 particularly include two of the wireless transceivers211 and 213, the present disclosure is intended to encompass numerousembodiments in which any arbitrary number of (e.g., more than two)wireless transceivers employing any arbitrary number of (e.g., two ormore) communication technologies are present. More particularly, in thepresent embodiment, the cellular transceiver 211 is configured toconduct cellular communications, such as 3G, 4G, 4G-LTE, vis-à-vis celltowers (not shown), albeit in other embodiments, the cellulartransceiver 211 can be configured to utilize any of a variety of othercellular-based communication technologies such as analog communications(using AMPS), digital communications (using CDMA, TDMA, GSM, iDEN, GPRS,EDGE, etc.), or next generation communications (using UMTS, WCDMA, LTE,IEEE 802.16, etc.) or variants thereof.

By contrast, the Wi-Fi transceiver 213 is a wireless local area network(WLAN) transceiver configured to conduct Wi-Fi communications inaccordance with the IEEE 802.11 (a, b, g, or n) standard with accesspoints. In other embodiments, the Wi-Fi transceiver 213 can instead (orin addition) conduct other types of communications commonly understoodas being encompassed within Wi-Fi communications such as some types ofpeer-to-peer (e.g., Wi-Fi Peer-to-Peer) communications. Further, inother embodiments, the Wi-Fi transceiver 213 can be replaced orsupplemented with one or more other wireless transceivers configured fornon-cellular wireless communications including, for example, wirelesstransceivers employing ad hoc communication technologies such as HomeRF(radio frequency), Home Node B (3G femtocell), Bluetooth, or otherwireless communication technologies such as infrared technology.Although in the present embodiment each of the wireless transceivers 201serves as or includes both a respective transmitter and a respectivereceiver, it should be appreciated that the wireless transceivers arealso intended to encompass one or more receiver(s) that are distinctfrom any transmitter(s), as well as one or more transmitter(s) that aredistinct from any receiver(s). In one example embodiment encompassedherein, the wireless transceiver 201 includes at least one receiver thatis a baseband receiver.

Exemplary operation of the wireless transceivers 201 in conjunction withothers of the components 200 of the mobile device 100 can take a varietyof forms and can include, for example, operation in which, uponreception of wireless signals (as provided, for example, by remotedevice(s)), the internal components detect communication signals and thetransceivers 201 demodulate the communication signals to recoverincoming information, such as voice or data, transmitted by the wirelesssignals. After receiving the incoming information from the transceivers201, the processors 203 format the incoming information for the one ormore output components 207. Likewise, for transmission of wirelesssignals, the processors 203 format outgoing information, which can butneed not be activated by the input components 209, and convey theoutgoing information to one or more of the wireless transceivers 201 formodulation so as to provide modulated communication signals to betransmitted. The wireless transceiver(s) 201 convey the modulatedcommunication signals by way of wireless (as well as possibly wired)communication links to other devices (e.g., remote devices). Thewireless transceivers 201 in one example allow the mobile device 100 toexchange messages with remote devices, for example, a remote networkentity (not shown) of a cellular network or WLAN network. Examples ofthe remote network entity include an application server, web server,database server, or other network entity accessible through the wirelesstransceivers 201 either directly or indirectly via one or moreintermediate devices or networks (e.g., via a WLAN access point, theInternet, LTE network, or other network).

Depending upon the embodiment, the output and input components 207, 209of the components 200 can include a variety of visual, audio, ormechanical outputs. For example, the output device(s) 207 can includeone or more visual output devices such as a cathode ray tube, liquidcrystal display, plasma display, video screen, incandescent light,fluorescent light, front or rear projection display, and light emittingdiode indicator, one or more audio output devices such as a speaker,alarm, or buzzer, or one or more mechanical output devices such as avibrating mechanism or motion-based mechanism. Likewise, by example, theinput device(s) 209 can include one or more visual input devices such asan optical sensor (for example, a camera lens and photosensor), one ormore audio input devices such as a microphone, and one or moremechanical input devices such as a flip sensor, keyboard, keypad,selection button, navigation cluster, touch pad, capacitive sensor,motion sensor, and switch.

As already noted, the various sensors 113 in the present embodiment canbe controlled by the sensor hub 224, which can operate in response to orindependent of the processor(s) 203. Examples of the various sensors 113may include, but are not limited to, power sensors, temperature sensors,pressure sensors, moisture sensors, ambient noise sensors, motionsensors (e.g., accelerometers or Gyro sensors), light sensors, proximitysensors (e.g., a light detecting sensor, an ultrasound transceiver or aninfrared transceiver), other touch sensors, altitude sensors, one ormore location circuits/components that can include, for example, aGlobal Positioning System (GPS) receiver, a triangulation receiver, anaccelerometer, a tilt sensor, a gyroscope, or any other informationcollecting device that can identify a current location or user-deviceinterface (carry mode) of the mobile device 100.

With respect to the processor(s) 203, the processor(s) can include anyone or more processing or control devices such as, for example, amicroprocessor, digital signal processor, microcomputer,application-specific integrated circuit, etc. The processors 203 cangenerate commands, for example, based on information received from theone or more input components 209. The processor(s) 203 can process thereceived information alone or in combination with other data, such asinformation stored in the memories 205. Thus, the memories 205 of thecomponents 200 can be used by the processors 203 to store and retrievedata.

Further, the memories (or memory portions) 205 of the components 200 canencompass one or more memory devices of any of a variety of forms (e.g.,read-only memory, random access memory, static random access memory,dynamic random access memory, etc.), and can be used by the processors203 to store and retrieve data. In some embodiments, one or more of thememories 205 can be integrated with one or more of the processors 203 ina single device (e.g., a processing device including memory orprocessor-in-memory (PIM)), albeit such a single device will stilltypically have distinct portions/sections that perform the differentprocessing and memory functions and that can be considered separatedevices. The data that is stored by the memories 205 can include, butneed not be limited to, operating systems, applications, andinformational data.

Each operating system includes executable code that controls basicfunctions of the mobile device 100, such as interaction among thevarious components included among the components 200, communication withexternal devices or networks via the wireless transceivers 201 or thedevice interface 215, and storage and retrieval of applications anddata, to and from the memories 205. Each application includes executablecode that utilizes an operating system to provide more specificfunctionality, such as file system service and handling of protected andunprotected data stored in the memories 205. Such operating system orapplication information can include software update information (whichcan be understood to potentially encompass updates to eitherapplication(s) or operating system(s) or both). As for informationaldata, this is non-executable code or information that can be referencedor manipulated by an operating system or application for performingfunctions of the mobile device 100.

It is to be understood that FIG. 2 is provided for illustrative purposesonly and for illustrating components of an mobile device in accordancewith various embodiments, and is not intended to be a complete schematicdiagram of the various components required for an mobile device.Therefore, an mobile device can include various other components notshown in FIG. 2, or can include a combination of two or more componentsor a division of a particular component into two or more separatecomponents, and still be within the scope of the disclosed embodiments.

Turning to FIG. 3, a process flow 300 illustrates a method for voicetraining recognition that may be performed by the mobile device 100,according to an embodiment. The mobile device 100 enters (302) a voicetraining mode. The voice training mode is a user interface or series ofuser interfaces of the mobile device 100 that allows the user to providea voice training sample. During training, the user may follow a seriesof explanatory steps, where the user is informed about how to locate anenvironment that is conducive to voice training, which command he or sheis to speak, and that he or she will be taken through multiple stepsduring the training process. For example, the mobile device 100 promptsthe user to speak a trigger word, trigger phrase (e.g., “OK GoogleNow”), or other word(s) that provide a basis for voice recognition, aswill be apparent to those skilled in the art. The mobile device 100records (304) a voice training sample for the user.

The mobile device 100 determines (306) a sample background noise levelfor the voice training sample. The sample background noise level is anindicator of background noise within the voice training sample. In oneexample, the sample background noise level is a numeric indicator, suchas a number of decibels of noise as an average power. In this case, thesample background noise level may be a decibel value with respect to anoverload point for the microphone 111 (e.g., −60dB or −40 dB). Inanother example, the sample background noise level is a tieredindicator, such as “High”, “Medium”, or “Low”. Other indicators for thesample background noise level will be apparent to those skilled in theart. The mobile device 100 in one example determines the samplebackground noise level by analyzing the voice training sample with avalley signal detector. In another example, the mobile device 100determines the sample background noise level by analyzing the voicetraining sample with a voice activity detector. For example, the mobiledevice 100 determines the sample background noise level for the voicetraining sample based on a signal level of a portion of the voicetraining sample that corresponds to a non-voice indication from thevoice activity detector.

The mobile device 100 may perform additional processing on the voicetraining sample (or intermediate data based on the voice trainingsample) to determine the sample background noise level, such asaveraging or smoothing. In one example, the mobile device 100 determinesthe sample background noise level based on a voice signal level for thevoice training sample, for example, as a noise to signal ratio or noiseto signal differential value. The mobile device 100 may determine thevoice signal level with a peak signal detector or a voice activitydetector.

The mobile device 100 also determines (308) a sample background noisetype for the voice training sample. The sample background noise type isan indicator of other audio characteristics of the background noise,such as noise distribution, variance, or deviation. The mobile device100 in example determines whether the sample background noise type is astationary noise type (e.g., road noise inside a moving car or fan noisefrom a nearby computer) or non-stationary noise type (e.g., sound from atelevision or conversation). Non-stationary noise generally has a highervariance in signal level (e.g., signal peaks when speaking and signalvalleys between sentences) than stationary noise. In other embodiments,the mobile device 100 may be configured to use other types of noise, aswill be apparent to those skilled in the art.

Upon determination (306, 308) of the sample background noise level andtype, the mobile device 100 determines (310) whether the samplebackground noise level has met (e.g., is less than or equal to) anindicator threshold value. The indicator threshold value is an indicatorof quality for the voice training sample. The indicator threshold valuein one example is a predetermined value, such as −40 dB. In anotherexample, the mobile device 100 selects the indicator threshold valuebased on the sample background noise type. In this case, the mobiledevice 100 may select a lower indicator threshold value for anon-stationary noise type than for a stationary noise type. While thedetermination (310) is shown as being performed on a recorded voicetraining sample, in other implementations the mobile device 100 performsthe determination (310) on an audio input signal (e.g., substantially inreal-time).

If the sample background noise level meets the indicator threshold value(YES at 310), the process 300 ends (e.g., the mobile device 100 proceedswith the voice training). If the sample background noise level does notmeet the indicator threshold value (NO at 310), the mobile device 100interrupts (312) the voice training mode to enter a noise indicator modebased on the sample background noise level and the sample backgroundnoise type. During the noise indicator mode, the mobile device 100displays (314) a noise indicator interface 400 (FIG. 4), for example, onthe display 105. The noise indicator interface 400 indicates that theambient or background noise is too high to continue, and that the usermust move to a quieter location in order to continue. The mobile device100 receives (316) an audio input signal (e.g., from the microphone 111)during the noise indicator mode. The mobile device 100 determines (318)a current background noise level for the audio input signal and updatesthe noise indicator interface 400, as described herein. In one example,the mobile device 100 updates the current background noise levelsubstantially in real-time. The mobile device 100 may determine thecurrent background noise level with one or more of the methods describedabove for determination (306) of the sample background noise level.

The mobile device 100 determines (320) whether a continuation input isreceived from the user while a continuation indicator 404 (FIG. 4) isenabled. The user provides the continuation input to indicate that theywish to proceed with the voice training mode (e.g., an interaction witha button or touch screen display, voice command, or other input). Asdescribed herein, the mobile device 100 enables or disables thecontinuation indicator 404 based on the current background noise level.In one example, the mobile device 100 disables the continuationindicator 404 to prevent the user from providing the continuation input.If the continuation input is received while the continuation indicator404 is enabled, the mobile device 100 returns (322) to the voicetraining mode. The mobile device 100 stays in the noise indicator modeand displays (314) the noise indicator interface 400 until thecontinuation input is received while the continuation indicator 404 isenabled. In alternate implementations, the user may cancel the noiseindicator mode by canceling the voice training mode (for example, tostop the voice training mode so that they may train at another time).

Turning to FIGS. 4A, 4B, 4C, and 4D, the noise indicator interface 400is shown represented as views 410, 420, 430, and 440 taken at differenttimes. The mobile device 100 displays (314) the noise indicatorinterface 400 during the noise indicator mode. In the examples shown inFIGS. 4A, 4B, 4C, and 4D, the noise indicator interface 400 includes anoise indicator 402, the continuation indicator 404, and optionally aninformation display 406.

The noise indicator 402 as shown in FIG. 4 is a dial-type indicator witha “needle” that corresponds to the current background noise level. Themobile device 100 in one example updates the needle as it determines thecurrent background noise level. In this case, the noise indicator 402may include one or more indicator thresholds, such as indicatorthresholds 408 and 409. In one example, the noise indicator 402indicates a range of values for the current background noise level, suchas −60 dB to 0 dB. In this case, the indicator thresholds 408 and 409correspond to indicator threshold values of −40 dB and −20 dB,respectively. In another example, the noise indicator 402 indicates asimplified interface without dB values. In this case and as shown inFIG. 4, the noise indicator interface 400 includes two or moresub-ranges indicated by user-friendly text, such as below the indicatorthreshold 408 (“Quiet”), between the indicator thresholds 408 and 409(“Noisy”), and above the indicator threshold 409 (“Loud”).

The continuation indicator 404 in one example is a user interface button(“Continue”), menu item, or other user interface component. The mobiledevice 100 initially disables the continuation indicator 404 when thenoise indicator interface 400 is displayed to prevent the user fromproceeding back to the voice training mode. For example, the mobiledevice 100 displays the continuation indicator 404 as a “greyed out” orinactive interface component, as shown in views 410, 420, and 430. Asdescribed above, the mobile device 100 updates the noise indicator 402with the current background noise level. When the current backgroundnoise level for the received (316) audio input signal meets theindicator threshold value (e.g., the indicator threshold value 408), themobile device 100 enables the continuation indicator 404 (as shown inview 440), and thus allowing the user to proceed by providing acontinuation input that corresponds to the continuation indicator 404.

While two indicator thresholds 408 and 409 are shown, the mobile device100 in the present embodiment uses one indicator threshold and itscorresponding indicator threshold value for the determination (320) onwhether to enable the continuation indicator 404. The mobile device 100may use the same or different indicator threshold values for Steps 310and 320. The indicator threshold values may be predetermined or selectedbased on the sample background noise type.

The information display 406 in one example provides information aboutthe noise indicator mode. For example, the information display 406provides an indication of what the use should do in order for the mobiledevice 100 to enable the continuation indicator 404. As shown in FIG. 4,the information display 406 provides additional text to supplement thenoise indicator 402. In alternative implementations, the informationdisplay 406 includes images or graphics that indicate a desirable quietenvironment. As shown in FIG. 4A, the noise indicator 402 andinformation display 406 indicate that the current background noise levelis “too loud” and that the user should “Find a quiet place” in order torecord. As shown in FIGS. 4B and 4C, as the user moves to a new (e.g.,more quiet) environment, the mobile device 100 updates the noiseindicator 402 and the information display 406 to indicate that it isstill “noisy.” As shown in FIG. 4D, when the user has located asufficiently quiet area, the noise indicator 402 falls into the “quiet”range and the mobile device 100 enables the continuation indicator 404.

Based on the above description, if the background noise rises above theindicator threshold value during the training, the user interface willbegin to display a new screen that indicates that the ambient orbackground noise is too high to continue, and that the user must move toa quieter location in order to continue. This user interface willcontinue to display until the user exits the training, or until the userclicks ‘continue’. The ‘continue’ button is not clickable until thevolume level once again returns to below the threshold that had resultedin the UI appearing in the first place.

It can be seen from the foregoing that a method and system for voicerecognition training have been described. In view of the many possibleembodiments to which the principles of the present discussion may beapplied, it should be recognized that the embodiments described hereinwith respect to the drawing figures are meant to be illustrative onlyand should not be taken as limiting the scope of the claims. Therefore,the techniques as described herein contemplate all such embodiments asmay come within the scope of the following claims and equivalentsthereof.

The apparatus described herein may include a processor, a memory forstoring program data to be executed by the processor, a permanentstorage such as a disk drive, a communications port for handlingcommunications with external devices, and user interface devices,including a display, touch panel, keys, buttons, etc. When softwaremodules are involved, these software modules may be stored as programinstructions or computer readable code executable by the processor on anon-transitory computer-readable media such as magnetic storage media(e.g., magnetic tapes, hard disks, floppy disks), optical recordingmedia (e.g., CD-ROMs, Digital Versatile Discs (DVDs), etc.), and solidstate memory (e.g., random-access memory (RAM), read-only memory (ROM),static random-access memory (SRAM), electrically erasable programmableread-only memory (EEPROM), flash memory, thumb drives, etc.). Thecomputer readable recording media may also be distributed over networkcoupled computer systems so that the computer readable code is storedand executed in a distributed fashion. This computer readable recordingmedia may be read by the computer, stored in the memory, and executed bythe processor.

The disclosed embodiments may be described in terms of functional blockcomponents and various processing steps. Such functional blocks may berealized by any number of hardware and/or software components configuredto perform the specified functions. For example, the disclosedembodiments may employ various integrated circuit components, e.g.,memory elements, processing elements, logic elements, look-up tables,and the like, which may carry out a variety of functions under thecontrol of one or more microprocessors or other control devices.Similarly, where the elements of the disclosed embodiments areimplemented using software programming or software elements, thedisclosed embodiments may be implemented with any programming orscripting language such as C, C++, JAVA®, assembler, or the like, withthe various algorithms being implemented with any combination of datastructures, objects, processes, routines or other programming elements.Functional aspects may be implemented in algorithms that execute on oneor more processors. Furthermore, the disclosed embodiments may employany number of conventional techniques for electronics configuration,signal processing and/or control, data processing and the like. Finally,the steps of all methods described herein may be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context.

For the sake of brevity, conventional electronics, control systems,software development and other functional aspects of the systems (andcomponents of the individual operating components of the systems) maynot be described in detail. Furthermore, the connecting lines, orconnectors shown in the various figures presented are intended torepresent exemplary functional relationships and/or physical or logicalcouplings between the various elements. It should be noted that manyalternative or additional functional relationships, physical connectionsor logical connections may be present in a practical device. The words“mechanism”, “element”, “unit”, “structure”, “means”, “device”,“controller”, and “construction” are used broadly and are not limited tomechanical or physical embodiments, but may include software routines inconjunction with processors, etc.

No item or component is essential to the practice of the disclosedembodiments unless the element is specifically described as “essential”or “critical”. It will also be recognized that the terms “comprises,”“comprising,” “includes,” “including,” “has,” and “having,” as usedherein, are specifically intended to be read as open-ended terms of art.The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless the context clearly indicates otherwise.In addition, it should be understood that although the terms “first,”“second,” etc. may be used herein to describe various elements, theseelements should not be limited by these terms, which are only used todistinguish one element from another. Furthermore, recitation of rangesof values herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein.

The use of any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosedembodiments and does not pose a limitation on the scope of the disclosedembodiments unless otherwise claimed. Numerous modifications andadaptations will be readily apparent to those of ordinary skill in thisart.

We claim:
 1. A method in a mobile device for voice recognition training,the method comprising: entering a voice training mode; recording a voicetraining sample for a user of the mobile device; interrupting the voicetraining mode to enter a noise indicator mode based on a samplebackground noise level for the voice training sample and a samplebackground noise type for the voice training sample; returning to thevoice training mode from the noise indicator mode when the user providesa continuation input that indicates a current background noise levelmeets an indicator threshold value.
 2. The method of claim 1 furthercomprising: receiving an audio input signal in the noise indicator mode;displaying a noise indicator interface with a noise indicator thatcorresponds to a current background noise level for the audio inputsignal, an indicator threshold that corresponds to the indicatorthreshold value, and a disabled continuation indicator; enabling thecontinuation indicator when the current background noise level meets theindicator threshold value; disabling the continuation indicator when thecurrent background noise level does not meet the indicator thresholdvalue; updating the noise indicator with the current background noiselevel while in the noise indicator mode; wherein returning to the voicetraining mode comprises: returning from the noise indicator mode whenthe user provides the continuation input and the continuation indicatoris enabled.
 3. The method of claim 2 further comprising selecting theindicator threshold value based on the sample background noise type. 4.The method of claim 3 wherein selecting the indicator threshold valuecomprises selecting a plurality of indicator threshold values thatincludes the indicator threshold value; wherein displaying the noiseindicator interface comprises displaying the noise indicator, aplurality of indicator thresholds that correspond to the plurality ofindicator threshold values, and the disabled continuation indicator. 5.The method of claim 3 wherein selecting the indicator threshold valuecomprises: selecting a first indicator threshold value if the samplebackground noise type is a stationary noise type; selecting a secondindicator threshold value if the sample background noise type is anon-stationary noise type, wherein the second indicator threshold valueindicates a lower noise level than the first indicator threshold value.6. The method of claim 2 wherein updating the noise indicator comprisesupdating the noise indicator with the current background noise levelsubstantially in real-time.
 7. The method of claim 2 wherein updatingthe noise indicator comprises smoothing the current background noiselevel.
 8. The method of claim 1 further comprising determining thesample background noise level with a valley signal detector.
 9. Themethod of claim 1 further comprising: analyzing the voice trainingsample with a voice activity detector; determining the sample backgroundnoise level for the voice training sample based on a signal level of aportion of the voice training sample that corresponds to a non-voiceindication from the voice activity detector.
 10. The method of claim 1further comprising determining whether the sample background noise typeis a stationary type or a non-stationary type.
 11. A mobile device forvoice recognition training, the mobile device comprising: a microphone;a non-transitory memory; a processor configured to retrieve instructionsfrom the memory; wherein the mobile device is configured to: enter avoice training mode; record a voice training sample for a user of themobile device; interrupt the voice training mode to enter a noiseindicator mode based on a sample background noise level for the voicetraining sample and a sample background noise type for the voicetraining sample; return to the voice training mode from the noiseindicator mode when the user provides a continuation input thatindicates a current background noise level meets an indicator thresholdvalue.
 12. The mobile device of claim 11 wherein mobile device isconfigured to: receive an audio input signal in the noise indicatormode; display a noise indicator interface with a noise indicator thatcorresponds to a current background noise level for the audio inputsignal, an indicator threshold that corresponds to the indicatorthreshold value, and a disabled continuation indicator; enable thecontinuation indicator when the current background noise level meets theindicator threshold value; disable the continuation indicator when thecurrent background noise level does not meet the indicator thresholdvalue; update the noise indicator with the current background noiselevel while in the noise indicator mode; return from the noise indicatormode when the user provides the continuation input and the continuationindicator is enabled.
 13. The mobile device of claim 12 wherein themobile device is configured to select the indicator threshold valuebased on the sample background noise type.
 14. The mobile device ofclaim 13 wherein the mobile device is configured to: select a pluralityof indicator threshold values that includes the indicator thresholdvalue; display the noise indicator, a plurality of indicator thresholdsthat correspond to the plurality of indicator threshold values, and thedisabled continuation indicator.
 15. The mobile device of claim 13wherein the mobile device is configured to: select a first indicatorthreshold value if the sample background noise type is a stationarynoise type; select a second indicator threshold value if the samplebackground noise type is a non-stationary noise type, wherein the secondindicator threshold value indicates a lower noise level than the firstindicator threshold value.
 16. The mobile device of claim 12 wherein themobile device is configured to update the noise indicator with thecurrent background noise level substantially in real-time.
 17. Themobile device of claim 12 wherein the mobile device is configured tosmooth the current background noise level.
 18. The mobile device ofclaim 11 wherein the mobile device is configured to determine the samplebackground noise level with a valley signal detector.
 19. The mobiledevice of claim 11 wherein the mobile device is configured to: analyzethe voice training sample with a voice activity detector; determine thesample background noise level for the voice training sample based on asignal level of a portion of the voice training sample that correspondsto a non-voice indication from the voice activity detector.
 20. Themobile device of claim 11 wherein the mobile device is configured todetermine whether the sample background noise type is a stationary typeor a non-stationary type.